NYCU webinar on
Scientific Machine Learning
National Yang Ming Chiao Tung University, Taiwan
Scientific Machine Learning (SciML) is an emerging interdisciplinary field of research that integrates traditional scientific disciplines with machine learning methods to solve complex scientific problems. The primary goal of SciML is to enable researchers to leverage the power of machine learning to accelerate scientific discovery and to build predictive models that can simulate and optimize complex scientific systems. Scientific machine learning methods can be applied to a wide range of scientific fields, including applied mathematics, physics, chemistry, biology, geoscience, climate science, and materials science, among others.
The purpose of this webinar is to bring together researchers and practitioners from various fields to exchange knowledge and ideas on the latest advances in machine learning and its application in scientific research.
Organizers: Ming-Chih Lai (賴明治) Ming-Cheng Shiue (薛名成) Te-Sheng Lin (林得勝)
Upcoming talks (in TPE time)
Webex link
Title: Towards Hybrid Neural Solvers based on Numerical Simulations and Deep Learning
Abstract:
This presentation will target recent advancements from the area of deep learning for physics simulations. A key focus is on the utilization of numerical solvers capable of providing gradient information, i.e. "differentiable simulators". These solvers seamlessly integrate with deep learning algorithms, presenting several advantages in practical scenarios, particularly in the context of flow simulations. However, the availability of gradient computation is not ubiquitous in many existing fluid simulation environments. Consequently, I will demonstrate a strategic approach to leverage non-differentiable simulators, serving as a transitional step and a middle ground in this context. As an outlook, I will explore the potential integration of these methods with diffusion modeling techniques, which offer powerful tools for handling uncertainties.
Sponsors:
1) College of Sciences, National Yang Ming Chiao Tung University, Taiwan
Organizers:
1) Ming-Chih Lai 賴明治, Chair Professor and Dean of College of Sciences,
Department of Applied Mathematics, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
2) Ming-Cheng Shiue 薛名成, Associate Professor,
Department of Applied Mathematics, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
3) Te-Sheng Lin 林得勝, Associate Professor,
Department of Applied Mathematics, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan